The present invention relates generally to process control systems and, more particularly, to optimizing the use of a model predictive controller in a process control system.
Process control systems, such as distributed or scalable process control systems like those used in chemical, petroleum or other processes, typically include one or more process controllers communicatively coupled to each other, to at least one host or operator workstation and to one or more field devices via analog, digital or combined analog/digital buses. The field devices, which may be, for example valves, valve positioners, switches and transmitters (e.g., temperature, pressure and flow rate sensors), perform functions within the process such as opening or closing valves and measuring process parameters. The process controller receives signals indicative of process measurements made by the field devices and/or other information pertaining to the field devices, uses this information to implement a control routine and then generates control signals which are sent over the buses to the field devices to control the operation of the process. Information from the field devices and the controller is typically made available to one or more applications executed by the operator workstation to enable an operator to perform any desired function with respect to the process, such as viewing the current state of the process, modifying the operation of the process, etc.
In the past, conventional field devices were used to send and receive analog (e.g., 4 to 20 milliamp) signals to and from the process controller via an analog bus or analog lines. These 4 to 20 ma signals were limited in nature in that they were indicative of measurements made by the device or of control signals generated by the controller required to control the operation of the device. However, in the past decade or so, smart field devices including a microprocessor and a memory have become prevalent in the process control industry. In addition to performing a primary function within the process, smart field devices store data pertaining to the device, communicate with the controller and/or other devices in a digital or combined digital and analog format, and perform secondary tasks such as self-calibration, identification, diagnostics, etc. A number of standard and open smart device communication protocols such as the HART(copyright), PROFIBUS(copyright), WORLDFIP(copyright), Device-Net(copyright), and CAN protocols, have been developed to enable smart field devices made by different manufacturers to be used together within the same process control network.
Moreover, there has been a move within the process control industry to decentralize process control functions. For example, the all-digital, two-wire bus protocol promulgated by the Fieldbus Foundation, known as the FOUNDATION(trademark) Fieldbus (hereinafter xe2x80x9cFieldbusxe2x80x9d) protocol uses function blocks located in different field devices to perform control operations previously performed within a centralized controller. In particular, each Fieldbus field device is capable of including and executing one or more function blocks, each of which receives inputs from and/or provides outputs to other function blocks (either within the same device or within different devices), and performs some process control operation, such as measuring or detecting a process parameter, controlling a device or performing a control operation, like executing a proportional-integral-derivative (PID) control routine. The different function blocks within a process control system are configured to communicate with each other (e.g., over a bus) to form one or more process control loops, the individual operations of which are spread throughout the process and are, thus, decentralized.
Process controllers are typically programmed to execute different algorithms, sub-routines or control loops (which are all control routines) for each of a number of different loops defined for, or contained within a process, such as flow control loops, temperature control loops, pressure control loops, etc. Generally speaking, each such control loop includes one or more input blocks, such as an analog input (AI) function block, a single-output control block, such as a proportional-integral-derivative (PID) or a fuzzy logic control function block, and a single output block, such as an analog output (AO) function block. These control loops typically perform single-input/single-output control because the control block creates a single output used to control a single process input, such as a valve position, etc. However, in certain cases, the use of a number of independently operating, single-input/single-output control loops is not very effective because the process variables being controlled are affected by more than a single process input and, in fact, each process input may affect the state of many process outputs. An example of this might occur in, for example, a process having a tank being filled by two input lines, and being emptied by a single output line, each line being controlled by a different valve, and in which the temperature, pressure and throughput of the tank are being controlled to be at or near desired values. As indicated above, the control of the throughput, the temperature and the pressure of the tank may be performed using a separate throughput control loop, a separate temperature control loop and a separate pressure control loop. However, in this situation, the operation of the temperature control loop in changing the setting of one of the input valves to control the temperature within the tank may cause the pressure within the tank to increase, which, for example, causes the pressure loop to open the outlet valve to decrease the pressure. This action may then cause the throughput control loop to close one of the input valves, thereby affecting the temperature and causing the temperature control loop to take some other action. As will be understood in this example, the single-input/single-output control loops cause the process outputs (in this case, throughput, temperature and pressure) to behave in an unacceptable manner wherein the outputs oscillate without ever reaching a steady state condition.
Model predictive control or other types of advanced control are used to perform control in these types of situations wherein controlled process variables affect more than one process input and wherein each process input affects more than one process output. Generally, model predictive control is a multiple-input/multiple output control strategy in which the effects of changing each of a number of process inputs on each of a number of process outputs is measured and these measured responses are then used to create a model of the process. The model of the process is inverted mathematically and is then used as a multiple-input/multiple-output controller to control the process outputs based on changes made to the process inputs. In some cases, the process model includes a process output response curve for each of the process inputs and these curves may be created based on a series of, for example, pseudo-random step changes delivered to each of the process inputs. These response curves can be used to model the process in known manners. Model predictive control is known in the art and, as a result, the specifics thereof will not be described herein. However, model predictive control is described generally in Qin, S. Joe and Thomas A. Badgwell, xe2x80x9cAn Overview of Industrial Model Predictive Control Technology,xe2x80x9d AIChE Conference, 1996.
Model predictive control may further be used to optimize a selected process input variable such that the process is controlled to maximize and/or minimize the variable selected for optimization. Process input variables that are selected for optimization may include, for example, the process input variables that have the greatest impact on improving the economic value of the process (e.g. process throughput), or the variables that have the greatest impact on improving the quality of the process output. Typically, it is desirable to maximize process throughput and product value and to minimize raw material cost.
In the course of optimizing, a user or an operator generally designates a process input variable to be optimized and determines an optimal process operating point where the designated process input variable is optimized. Of course, the optimal process operating point must also coincide with a process operating point that allows a desired process outcome to be achieved. To achieve the desired process outcome, one or more process output variables must be maintained at predetermined levels or settings, also referred to as setpoints. The process output variables are maintained at their respective setpoints by the proper adjustment of a set of process input variables. Unfortunately, optimal process control is more complex than simply operating the process at the optimal process operating point because the optimal process operating point typically exists at the limits of the process operation. More particularly, each of the process input variables has a physical constraint limit and if, while operating at the optimal process operating point, two or more of the process input variables reach or exceed a physical constraint limit associated therewith, then the process will enter into an undesirable state wherein the process controller will be unable to maintain the process output variables at their respective setpoints. However, as described above, the optimal process operating point involves operating the designated optimal process input variable at its maximum (or minimum) setting which is typically equal to its physical constraint limit. Thus, while operating at the optimal process operating point, one of the process input variables is already at its physical constraint limit such that if even one of the other process input variables reaches or exceeds its physical constraint limit, then the process enters the undesirable state wherein the controller is unable to maintain the process output variables at their respective setpoints. As a result, in addition to operating at the optimal process operating point, process optimization further requires monitoring the process input variables and taking action to prevent them from reaching or exceeding their constraint limits.
Conventional optimization control algorithms monitor the process variables while the process is operating to identify when one or more of the constraint limits is being approached. Preventative action, such as adjusting a process input, is then taken to avoid violating the constraint limit that is being approached. In a conventional control system, decision making about whether preventative action is required must be more conservative because the data upon which this decision making is based is real-time process data to which there is little time to react. As a result, the process must operate farther from the desired or optimized constraint limits, which compromises the degree of optimization achieved by the process. Moreover, preventative action is not always necessary every time a process variable approaches a constraint limit because, in some instances, even without preventative action, the constraint limit will not actually be violated. However, in conventional control systems, the collection of real-time process data does not afford insight as to whether the constraint limit that is being approached will actually be violated. Therefore, every constraint limit approach must be treated as a potential violation and preventative action must be taken. As a result, at least some of the preventative actions are undertaken unnecessarily which also causes optimization to be comprised unnecessarily.
In addition, existing control systems that offer optimized control, typically use a separate optimization control algorithm to achieve the optimization. The need for a separate optimization control algorithm increases the complexity of the overall control scheme and increases the complexity of the control apparatus needed to achieve the overall control scheme.
The present invention is directed to a multi-input/multi-output logic controller for controlling a process, wherein each controller input is adapted to receive a process output signal and wherein each controller output is adapted to provide a process control signal to a process control device. To enable optimal control of the process, a a selected one of the controller outputs is coupled to a further controller input having an associated setpoint and the controller uses the associated setpoint to maintain the selected controller output at its constraint limit.
According to one aspect of the invention, a multi-input/multi-output logic device includes a plurality of inputs that are adapted to receive process output signals, a further input having an associated setpoint, and a multiplicity of outputs that are adapted to provide process control signals for controlling a process. The logic device further includes a feedback path that connects a selected one of the outputs to the further input and a logic unit, such as a model predictive controller or a fuzzy logic controller, that is configured to produce the multiplicity of outputs using the plurality of inputs and the further input. The logic unit is adapted to adjust the selected output until the further input has reached the associated setpoint which may be set equal to a constraint limit of the selected output.
According to a further aspect of the invention, a multiplicity of the outputs may have an associated constraint limit and the logic unit of the logic device may include a setpoint adjustment unit that adjusts the setpoint from a first setpoint setting to a second setpoint setting when one of the outputs, other than the selected output, is going to reach or exceed the associated constraint limit for that output. The first setpoint setting may correspond to the constraint limit of the selected output and the second setpoint setting may correspond to a value that is relaxed with respect to the constraint limit of the selected output.
According to a still further aspect of the invention, the setpoint adjustment unit may be further adapted to adjust the setpoint from the second setpoint setting to the first setpoint setting when the one of the outputs is no longer going to reach or exceed the associated constraint limit for that output.
According to yet another aspect of the invention, the logic device may further include a prediction unit that predicts a future value for each of the outputs, other than the selected output, and a comparison unit that compares the future value for each of the multiplicity of outputs to the constraint limit associated with that output to determine whether any one of the outputs, other than the selected output, is going to reach or exceed the associated constraint limit for that output.
According to a further aspect of the invention, a method for controlling a process uses a multi-input/multi-output logic device that has a plurality of inputs adapted to receive process output signals and a multiplicity of outputs, each having an associated constraint limit, adapted to provide process control signals for controlling the process. The method includes the steps of connecting a selected one of the outputs to a further input of the logic device, setting a setpoint that is associated with the further input to a first setpoint setting and adjusting the selected output until the further input has reached the first setpoint setting. The method further includes the steps of calculating a future value for each of the multiplicity of outputs, other than the selected output, comparing the future value calculated for each output to a constraint limit associated with the output to determine whether any one of the outputs, other than the selected output, is going to reach or exceed the associated constraint limit for that output. The step of adjusting the setpoint from the first setpoint setting to a second setpoint setting is performed when one of the outputs, other than the selected output, is going to reach or exceed the associated constraint limit for that output.
According to yet another aspect of the invention, the method may further include the step of adjusting the setpoint from the second setpoint to the first setpoint when the step of comparing determines that the one of the outputs is no longer to going to reach or exceed the associated constraint limit for that output.